Smart battery management in EVs using IoT, blockchain, and machine learning

Electric Vehicles (EVs) have gained significant attention in recent years, mostly owing to the advancements in electric motors and minimal impact on the environment. In the same way, the rapid development of the Internet of Things (IoT) facilitated the interconnection of an increasing number of devices. A significant challenge that EVs have presently is the restricted battery range and a shortage of charging or battery switching facilities. The Battery Management System (BMS) is significant in EVs since it regulates and monitors battery functionality, providing optimal efficiency and prolonging battery lifespan. Integrating IoT and Blockchain (BC) in BMS offers a promising approach to enhance energy efficiency and battery state evaluation. In this research, a BMS model for EVs using IoT, blockchain and machine learning (ML) is developed. The IoT sensors have been equipped to the electric vehicles to gather data such as the level of charging, the distance to be travelled, and the EVs location. For determining the cost of charging, this information is stored and processed by a database, and then it is given as input to the Extreme Gradient Boosting (XGBoost) classifier. After that, the power scheduling based on the Grey Wolf Optimization (GWO) technique processes it to determine the location of the charging station, as well as the time and space of charging that is closest. Finally, this data is stored in blocks to prevent intruders from gaining access to electric vehicles and to ensure that pricing transactions between users and charging stations are carried out in a secure manner. For these secure transactions, a permissioned blockchain with homomorphic encryption (HE) is implemented. The results show that the research work produced an enhanced BMS model with an accuracy rate of 97.36% in detecting close charging station and that it maintains a communication overhead with 35 ms, which was 14% lower than the compared models.